Teams Didn’t Design Complex Stacks, They Inherited Them

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Marketing teams don’t lack ambition. What they lack is room to breathe.

As channels multiply and customer behaviour shifts by the minute, the marketer’s role has quietly become more complex—and less creative. Too much energy is spent stitching systems together, chasing delayed data, and defending performance, while the work that actually builds customer relationships gets pushed to the margins.

Marketers are expected to move faster, personalise deeper, and prove impact in real time, often with tools that were never designed to work together. The result is decision fatigue, slower execution, and missed moments that matter.

“What’s holding marketers back isn’t talent or ambition; it’s the day-to-day friction of their legacy tech stack. Most teams didn’t design complex stacks; they inherited them. One tool for email. Another for push. Another for data. Another for AI. Over time, that turned into operational drag,” says Sam Allen, CEO, Iterable.

Sam shares how Iterable is helping marketers become masters of their craft, why embedded and explainable AI is essential for lifecycle marketing, and how brands can scale intelligence without losing their voice. 

Excerpts from the interview:

You’ve led large-scale transformation at Salesforce and now at Iterable. What lessons from your operational and Marine Corps experience guide how you scale and innovate at Iterable today?

Two lessons stand out: trust and servant leadership. You can’t move fast or win at scale if you don’t trust your people and your tools. In high-stakes environments, hesitation is failure. When trust is built early—in the team, the technology, and the operating model—execution gets faster, and decisions get sharper.

That mindset drives how we build at Iterable. Our customers rely on us in critical moments, at massive scale. Reliability and performance aren’t optional—they’re how we earn trust. And when trust is in place, innovation accelerates because teams can move fast and take smart risks.

The second lesson, and one honed while serving in the Marines, is servant leadership. My job as CEO is to serve the team—set clear intent, remove friction, and create the conditions for success. 

In turn, our teams serve our customers the same way. When leaders serve their teams and teams serve their customers, trust compounds—and the business moves faster.

Iterable aims to turn marketers into “masters of their craft.” What are the biggest friction points holding them back, and how should technology remove—not add—complexity?

What’s holding marketers back isn’t talent or ambition; it’s the day-to-day friction of their legacy tech stack.

Most teams didn’t design complex stacks; they inherited them. One tool for email. Another for push. Another for data. Another for AI. Over time, that turned into operational drag.

Today, marketers spend too much energy managing systems, reconciling data, and justifying results instead of doing the work that actually drives growth.

AI makes the problem impossible to ignore. Customer behaviour is changing in real time. Expectations from the business are higher than ever. But many teams are still operating on delayed data, manual workflows, and underutilised tools. 

Marketers feel that gap every day when they know what they want to do — but the tools can’t move fast enough. They know what they want to do, but their technology won’t let them move fast enough or execute on modern strategies.

Marketers need technology that removes busywork and simplifies complex tasks into a single workflow. It should reflect how they actually work. Not across patchwork tools, but inside one system that uses all available data, automation, and intelligence, powered by AI. 

When the platform absorbs the complexity, marketers can focus on judgment, creativity, and results.

How can marketers look at adding intelligence to their omnichannel messaging to meet customers “in the moment”, and what would this look like?

To meet customers in the moment, intelligence has to be embedded into the core of both the platform and the strategy—not added later as an afterthought. When AI has access to the right data across channels and touchpoints, it can guide execution in real time, not just analyse outcomes after the fact. 

That means dynamically optimising which channel to use, when to engage, and how often to message based on how customers are actually behaving. It reduces over-messaging, eliminates brittle manual rules, and keeps engagement relevant as behaviour shifts.

Embedded intelligence also moves teams from hindsight to foresight. Instead of reacting once a campaign is over, marketers can understand how different audiences are likely to perform against specific goals—whether that’s conversion, retention, or lifetime value—and adjust strategy before performance drops.

Just as important, this intelligence must be explainable. Marketers need visibility into the signals driving recommendations so they can build trust, apply human judgment, and stay aligned with brand and business objectives.

When intelligence is built in, grounded in real data, and transparent by design, it doesn’t add complexity —it removes it. The result is better decisions made faster, stronger customer experiences, and measurable impact on the bottom line.

How do you see Explainable AI working for lifecycle marketing, and what can marketers do to play a value-added role here?

Explainable AI is critical in lifecycle marketing because the goal is not short-term optimisation. It is long-term customer trust. Iterable has understood this since its inception. 

Marketers are accountable for the relationship, not just the metric, which means they need to understand why a system is making a decision, and not just accept the outcome.

In lifecycle marketing, explainability means visibility into how decisions are made across moments. Marketers should be able to see which signals informed a recommendation, how timing or channel choices were prioritised, and what behaviour the system is responding to. 

That transparency allows teams to validate decisions, align them with brand standards and business goals, and confidently apply AI across more of the customer lifecycle. 

Explainability also changes the marketer’s role in a meaningful way. When AI handles pattern detection and optimisation at scale, marketers create value by providing context and judgment. They decide when to lean in, when to override, and how to apply insights in a way that respects customer intent and experience. 

The strongest lifecycle programs come from this partnership. AI brings speed and clarity. Marketers bring strategy, creativity, and accountability. Together, they drive better outcomes without sacrificing trust. 

What advice would you give marketers who are worried about falling into the AI sameness trap when using generative AI for brand messaging?

AI does not make brands sound the same. Giving up control does.

Sameness shows up when marketers treat generative AI as a creative replacement instead of a strategic tool. When AI is used without clear intent, constraints, and ownership, it regresses to the mean. That is not an AI problem. That is a leadership problem.

AI is extremely good at managing scale and complexity. It can interpret massive volumes of customer signals, adapt messaging in real time, and support consistent engagement across channels. What it cannot do is define what a brand stands for or how it should sound in moments that matter. That responsibility stays with the marketer.

The strongest teams use AI to amplify distinction. They set the strategy, define the voice, and decide where variation matters. AI then helps deliver that intent consistently, even as customer behaviour shifts.

My advice would be to use AI to handle complexity. Keep humans accountable for meaning, judgment, and trust. That is how brands move faster without losing what makes them recognisable across the customer lifecycle.

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